About the Role
Join a research and engineering team building synthetic data systems for AI agents. You will develop methods and infrastructure that turn real-world workflows into high-quality training tasks, helping expand and evaluate model capabilities.
What You'll Do
Build pipelines that transform domain-specific workflows into realistic, structured, and challenging synthetic training tasks.
Collaborate with subject-matter experts to create tasks across professional and technical domains.
Design methods for generating diverse, realistic, and learnable tasks.
Develop tools to mutate, validate, and improve synthetic tasks.
Analyze model and agent performance to understand task effectiveness and identify failure modes.
Create metrics to assess task diversity, realism, learnability, and overall quality.
What We're Looking For
Two or more years of experience in software engineering, machine learning engineering, or AI research, with a technical background.
Hands-on experience applying synthetic data methods to build end-to-end data generation pipelines for AI or machine learning.
Proficiency in Python, Linux, and containerization tools such as Docker.
Experience with evaluation frameworks, benchmarks, or testing environments for AI agents or large language models.
Understanding of synthetic data quality, evaluation metrics, and limitations, backed by research or production work.
Experience building automated systems to generate, validate, mutate, or process structured data at scale.
Ability to own technical projects from conception through delivery in loosely defined problem spaces.
Strong attention to detail, communication skills, and ability to reason about task design, scoring, and edge cases.
Familiarity with reinforcement learning, agent workflows, or LLM post-training is helpful.
Compensation & Benefits
Annual salary range: $100,000 to $170,000 USD. Visa sponsorship is available.
Location
On-site in Singapore, Singapore.